Triple

T4470149
Position Surface form Disambiguated ID Type / Status
Subject Dueling DQN E98474 entity
Predicate introducedInPaper P513 FINISHED
Object Dueling Network Architectures for Deep Reinforcement Learning E98474 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Dueling Network Architectures for Deep Reinforcement Learning | Statement: [Dueling DQN, introducedInPaper, Dueling Network Architectures for Deep Reinforcement Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dueling Network Architectures for Deep Reinforcement Learning
Context triple: [Dueling DQN, introducedInPaper, Dueling Network Architectures for Deep Reinforcement Learning]
  • A. Dueling DQN chosen
    Dueling DQN is a deep reinforcement learning algorithm that separates state-value and advantage estimations within its neural network architecture to improve learning efficiency and stability over standard DQN.
  • B. Asynchronous Methods for Deep Reinforcement Learning
    "Asynchronous Methods for Deep Reinforcement Learning" is a 2016 DeepMind paper that introduced asynchronous parallel training techniques for deep reinforcement learning, most notably the A3C algorithm, enabling more stable and efficient learning without specialized hardware.
  • C. Atari deep Q-network
    The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
  • D. Prioritized Experience Replay DQN
    Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
  • E. Asynchronous Advantage Actor-Critic
    Asynchronous Advantage Actor-Critic is a deep reinforcement learning algorithm that trains multiple parallel agents to learn both policy and value functions efficiently and stably.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69b3454b4ae481908967426dd37284d6 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3569cd03c8190927c596bedb45ac8 completed March 13, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6377154bc819099362e8b28698dbe completed March 15, 2026, 4:37 a.m.
Created at: March 12, 2026, 11:34 p.m.